Segmentation of connected text using constrained neural networks

Shishani, Basel (1997) Segmentation of connected text using constrained neural networks. Masters by Research thesis, Queensland University of Technology.

Available via Document Delivery only – contact your library to place a request
If you are the author of this thesis, please contact

Impact and interest:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 36833
Item Type: QUT Thesis (Masters by Research)
Additional Information: Presented to the School of Computing Science, Queensland University of Technology.
Keywords: Optical pattern recognition Data processing, Neural networks (Computer science), Arabic alphabet, optical character recognition, constrained neural networks, combinatorial optimization, Hopfield network, Boltzmann machine, attributed graphs, graphs isomorphism, Arabic character recognition, feature extraction, thesis, masters
Institution: Queensland University of Technology
Copyright Owner: Copyright Basel Shishani
Deposited On: 22 Sep 2010 13:06
Last Modified: 11 Nov 2015 23:17

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page